Experimental demonstration of an analytic method for image reconstruction in optical tomography with large data sets

We report the first experimental test of an analytic image reconstruction algorithm for optical tomography with large data sets. Using a continuous-wave optical tomography system with 10^8 source-detector pairs, we demonstrate the reconstruction of an absorption image of a phantom consisting of a highly-scattering medium with absorbing inhomogeneities.Comment: 3 pages, 3 figure

Strength Development in Overmolded Structures

Overmolding of thermoplastic composites is a technology in which a thermoplastic composite is thermoformed and subsequently injection overmolded. Although the feasibility of the process is increasingly demonstrated, it is acknowledged that there is a lack of proper design tools that can be used for a right-the-first-time design strategy. Here, a modelling strategy is proposed for the prediction of the bond strength between a composite insert and an injected polymer. The development of the interface strength is affected by the process history as well, where the temperature and polymer chain mobility play an important role. In the model, the melting behavior of the polymer interface is described using the temperature evolution on the interface combined with experimentally determined polymer melting kinetics via flash differential scanning calorimetry (DSC). Dedicated test geometries were developed and manufactured to evaluate the bond strength under different loading conditions. Short beam strength experiments were used to study the flow length dependency of the interface strength and were correlated with the predicted melting evolution on the interface. The outcome was critically reviewed leading to preliminary guidelines for design, materials and processing as well as routes to further mature this technology

Prediction of Extreme Ultraviolet Variability Experiment (EVE)/Extreme Ultraviolet Spectro-Photometer (ESP) Irradiance from Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) Images Using Fuzzy Image Processing and Machine Learning

YesThe cadence and resolution of solar images have been increasing dramatically with the launch of new spacecraft such as STEREO and SDO. This increase in data volume provides new opportunities for solar researchers, but the efficient processing and analysis of these data create new challenges. We introduce a fuzzy-based solar feature-detection system in this article. The proposed system processes SDO/AIA images using fuzzy rules to detect coronal holes and active regions. This system is fast and it can handle different size images. It is tested on six months of solar data (1 October 2010 to 31 March 2011) to generate filling factors (ratio of area of solar feature to area of rest of the solar disc) for active regions and coronal holes. These filling factors are then compared to SDO/EVE/ESP irradiance measurements. The correlation between active-region filling factors and irradiance measurements is found to be very high, which has encouraged us to design a time-series prediction system using Radial Basis Function Networks to predict ESP irradiance measurements from our generated filling factors

Solar flare prediction using advanced feature extraction, machine learning and feature selection

YesNovel machine-learning and feature-selection algorithms have been developed to study: (i) the flare prediction capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); (ii) SMART's MF properties that are most significantly related to flare occurrence. Spatio-temporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine learning and feature selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare prediction capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast verification measures and compared with the prediction measures of one of the industry's standard technologies for flare prediction that is also based on machine learning - Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine learning has the potential to achieve more accurate flare prediction than ASAP. Feature selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of 6 MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties

Transforming growth factor beta induces sensory neuronal hyperexcitability, and contributes to pancreatic pain and hyperalgesia in rats with chronic pancreatitis

Background: Transforming growth factor beta (TGFβ) is upregulated in chronic inflammation, where it plays a key role in wound healing and promoting fibrosis. However, little is known about the peripheral effects of TGFβ on nociception.Methods: We tested the in vitro effects of TGFβ1 on the excitability of dorsal root ganglia (DRG) neurons and the function of potassium (K) channels. We also studied the effects of TGFβ1 infusion on pain responses to noxious electrical stimulation in healthy rats as well as the effects of neutralization of TGFβ1 on evoked pain behaviors in a rat model of chronic pancreatitis.Results: Exposure to TGFβ1 in vitro increased sensory neuronal excitability, decreased voltage-gated A-type K+ currents (IA) and downregulated expression of the Kv1.4 (KCNA4) gene. Further TGFβ1 infusion into the naïve rat pancreas in vivo induces hyperalgesia and conversely, neutralization of TGFβ1 attenuates hyperalgesia only in rats with experimental chronic pancreatitis. Paradoxically, TGFβ1 neutralization in naïve rats results in pancreatic hyperalgesia.Conclusions: TGFβ1 is an important and complex modulator of sensory neuronal function in chronic inflammation, providing a link between fibrosis and nociception and is a potentially novel target for the treatment of persistent pain associated with chronic pancreatitis. © 2012 Zhu et al.; licensee BioMed Central Ltd

Evaluation of the ICT Tuberculosis test for the routine diagnosis of tuberculosis

BACKGROUND: Rapid and accurate diagnosis of tuberculosis (TB) is crucial to facilitate early treatment of infectious cases and thus to reduce its spread. To improve the diagnosis of TB, more rapid diagnostic techniques such as antibody detection methods including enzyme-linked immunosorbent assay (ELISA)-based serological tests and immunochromatographic methods were developed. This study was designed to evaluate the validity of an immunochromatographic assay, ICT Tuberculosis test for the serologic diagnosis of TB in Antalya, Turkey. METHODS: Sera from 72 patients with active pulmonary (53 smear-positive and 19 smear-negative cases) and eight extrapulmonary (6 smear-positive and 2 smear-negative cases) TB, and 54 controls from different outpatient clinics with similar demographic characteristics as patients were tested by ICT Tuberculosis test. RESULTS: The sensitivity, specificity, and negative predictive value of the ICT Tuberculosis test for pulmonary TB were 33.3%, 100%, and 52.9%, respectively. Smear-positive pulmonary TB patients showed a higher positivity rate for antibodies than smear-negative patients, but the difference was not statistically significant. Of the eight patients with extrapulmonary TB, antibody was detected in four patients. CONCLUSION: Our results suggest that ICT Tuberculosis test can be used to aid TB diagnosis in smear-positive patients until the culture results are available

Malonylation of GAPDH is an inflammatory signal in macrophages.

Macrophages undergo metabolic changes during activation that are coupled to functional responses. The gram negative bacterial product lipopolysaccharide (LPS) is especially potent at driving metabolic reprogramming, enhancing glycolysis and altering the Krebs cycle. Here we describe a role for the citrate-derived metabolite malonyl-CoA in the effect of LPS in macrophages. Malonylation of a wide variety of proteins occurs in response to LPS. We focused on one of these, glyceraldehyde-3-phosphate dehydrogenase (GAPDH). In resting macrophages, GAPDH binds to and suppresses translation of several inflammatory mRNAs, including that encoding TNFα. Upon LPS stimulation, GAPDH undergoes malonylation on lysine 213, leading to its dissociation from TNFα mRNA, promoting translation. We therefore identify for the first time malonylation as a signal, regulating GAPDH mRNA binding to promote inflammation

A Balance of BMP and Notch Activity Regulates Neurogenesis and Olfactory Nerve Formation

Although the function of the adult olfactory system has been thoroughly studied, the molecular mechanisms regulating the initial formation of the olfactory nerve, the first cranial nerve, remain poorly defined. Here, we provide evidence that both modulated Notch and bone morphogenetic protein (BMP) signaling affect the generation of neurons in the olfactory epithelium and reduce the number of migratory neurons, so called epithelioid cells. We show that this reduction of epithelial and migratory neurons is followed by a subsequent failure or complete absence of olfactory nerve formation. These data provide new insights into the early generation of neurons in the olfactory epithelium and the initial formation of the olfactory nerve tract. Our results present a novel mechanism in which BMP signals negatively affect Notch activity in a dominant manner in the olfactory epithelium, thereby regulating neurogenesis and explain why a balance of BMP and Notch activity is critical for the generation of neurons and proper development of the olfactory nerve